Large Language Models as Visualization Agents for Immersive Binary Reverse Engineering

Immersive virtual reality (VR) offers affordances that may reduce cognitive complexity in binary reverse engineering (RE), enabling embodied and external cognition to augment the RE process through enhancing memory, hypothesis testing, and visual organization. In prior work, we applied a cognitive systems engineering approach to identify an initial set of affordances and implemented a VR environment to support RE through spatial persistence and interactivity. In this work, we extend that platform with an integrated large language model (LLM) agent capable of querying binary analysis tools, answering technical questions, and dynamically generating immersive 3D visualizations in alignment with analyst tasks. We describe the system architecture and our evaluation process and results. Our pilot study shows that while LLMs can generate meaningful 3D call graphs (for small programs) that align with design principles, output quality varies widely. This work raises open questions about the potential for LLMs to function as visualization agents, constructing 3D representations that reflect cognitive design principles without explicit training.